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The performance of meteorological data-based methods to forecast plant diseases strongly depends on temporal weather information. In this paper, a data analysis procedure is presented for finding the optimal starting time for forecasting net blotch density in spring barley based on meteorological data. For this purpose, changes in the information content of typically measured weather variables were systemically quantified in sliding time windows and with additionally generated mathematical transformations, namely with features. Signal-to-noise statistics were applied in a novel way as a metric for identifying the optimal starting time instance and the most important features to successfully distinguish between two net blotch densities during springtime itself. According to the results, the information content of meteorological data used in classifying between nine years with and four years without net blotch reached its maximum in Finnish weather conditions on the 41st day from the beginning of the growing season. Specifically, utilising weather data at 41–55 days from the beginning of the growing season maximises successful forecasting potential of net blotch density. It also seems that this time instance enables a linear classification task with a selected feature subset, since the averages of the metrics in two data groups differ statistically with a minimum 68% confidence level for nine days in a 14-day time window.

Cover crops (CCs) have aroused a great deal of interest as a multifunctional measure to improve the sustainability of agriculture. Understanding farmers’ views are important for future farm-scale implementation. A farmer survey was carried out in Finland in 2021 with the aims to gather farmers’ views on agronomic performance of CCs, their environmental impacts and contribution to climate smart agriculture, and understand how farmers’ views on CCs differed depending on farm/farmer characteristics. The farmers’ sample was conventional and organic farms that had selected CCs as a registered measure in 2020. 6493 farmers were invited to answer a questionnaire with 18 statements (a Likert scale, 5 answer choices), and 1130 responded (17.4%). A Cochran–Mantel–Haenszel test was used to measure the strength of the association between ten characteristics of the respondents and 18 statements. Farmers considered CCs to have wide-ranging benefits for soil conditions. Only 21% of farmers agreed that CCs increase the need for nitrogen fertilizer use. 49% of farmers agreed that CCs reduce weed problems. Farmers mostly agreed (ca. 80%) that CCs reduce nutrient leaching and erosion. They were in general more uncertain about CCs’ contribution to climate change mitigation (53% agreed), adaptation (51%), and resilience (58%). In agri-environmental schemes subsidies for use of CCs should aim large-scale implementation with two important target groups: younger farmers (≤50 years) as they were slightly more skeptical than older ones and farmers with less diverse land use as they were more doubtful of benefits provided by CCs.

The aim of this study was to investigate factors responsible for between-herd variation in milk production, when genetic variation is controlled. Quantitative information regarding farms’ production environment and animal welfare, as evaluated annually by veterinarians and health and feeding records, were the factors studied. Principal component analysis was used to reduce the production environment variables as well as the welfare data. Two linear regression models were devised. The first model used welfare indicators and veterinary treatments to predict the difference between herds’ observed and potential milk yield. The second model explained the residual of the first model by feeding and animal-based indicator data. Together, these two models explained 62% of the variance in milk yield differences between herds. Specifically, feeding of the herd was the most important factor, accounting for 67%, followed by the production environment/animal welfare (30%) and finally animal health, assessed through veterinary treatments, explained the remaining 3% of the variance. A poor welfare rating adversely affected milk production. Similarly, a low score for fatness at slaughter, poor milk quality and high mortality all showed a clear negative association with production. It was found that while feeding remains a major factor, production environment and animal welfare also have significant roles to play when it comes to production. Notably, those farms with major animal welfare problems were shown to display milk yield below the Finnish average.

Crop rotation and soil tillage are among key factors impacting cropping system productivity, pest management and soil health. To assess their role in northern cropping systems, we quantified the effects of crop rotation on spring wheat yield in different tillage systems based on a long-term (2005–2017) field experiment in southwestern Finland. In addition, effects of crop rotation on weeds, plant pathogens, and pest insects were assessed. Three types of crop rotation were compared: monoculture (spring wheat), 2-year rotation (spring wheat—turnip rape—spring wheat—barley) and 4-year rotation (spring wheat—turnip rape—barley—pea) under no-tillage and plowing. A diversified crop rotation improved spring wheat yield by up to 30% in no-tillage and by 13% under plowing compared with monoculture. Overall, the yield quantity and quality differences between crop rotations were higher in no-tillage plots than in plowed plots. The occurrence of weed species in spring wheat before herbicide control was highest in the four-year crop rotation and lowest in the wheat monoculture. For plant diseases, wheat leaf blotch disease severity, mainly caused by Pyrenophora tritici-repentis, was lowest in the most diverse crop rotation. On average, wheat leaf blotch disease severity was 20% less when wheat was grown every fourth year compared with wheat monoculture. The effect of crop rotation on stem and root diseases became apparent after 6 years of rotation and the disease index was lowest in the most diverse crop rotation. Neither rotation nor tillage affected the control need of wheat midge (Sitodiplosis mosellana). Based on our results, diverse crop rotations including cereals, oilseed crops, and legumes increase yield and reduce plant disease severity of spring wheat in Finland, with the magnitude being larger in no-tillage systems.

Cereal-based rotations dominate the prime production regions of Finland without any signs of noteworthy shifts towards more diverse systems. To estimate the potential for the future expansion of more diverse crop rotations we used multinomial logistic regression to model the probability that a field parcel would suit for crops other than cereals by acknowledging farmer's preferences in land allocation depending on field parcel characteristics (size, shape, slope, distance to farm center, proximity to waterway, soil type and ownership). This study covered ca. 700,000 field parcels in the prime production region and all the farm types. We identified parcels that were currently used for pure cereal rotations but were also suitable for the cultivation of more diverse crops, especially rapeseed and grain legumes as the most potential ones. There was substantial potential to shift from the current cereal rotations towards more diverse crop sequencing patterns in all farm types, but especially on pig, poultry and cereal farms, where some 18–20% of the cereal monoculture rotations could be shifted towards break crop rotations and 24–41% to diverse rotations. The diversification potential was dependent on the farm size and was higher on large farms with more suitable land for minor crops and with better logistic advantages. Hence, the ongoing increase in farm size and reduction in the number of farms could further support the transition towards more diverse crop sequencing. Diversity in both crop rotation and the agricultural landscape can be achieved simultaneously. Due to the characteristics of field parcels allocated to perennial grasslands and green-fallow rotations they were not suitable for diversifying arable crops. To enhance use of more diverse crop rotations, coherent policies and sufficient incentives are needed to encourage farmers to exploit the existing potential in a timely fashion despite the current socio-economically challenging situation that farmers are facing in Finland.

The information on the grass quantity and quality is needed for several times in a growing season for making optimal decisions about the harvesting time and the fertiliser rate, especially in northern countries, where grass swards quality declines and yield increases rapidly in the primary growth. We studied the potential of UAV-based photogrammetry and spectral imaging in grass quality and quantity estimation. To study this, a trial site with large variation in the quantity and quality parameters was established by using different nitrogen fertilizer application rates and harvesting dates. UAV-based remote sensing datasets were captured four times during the primary growth season in June 2017 and agricultural reference measurements including dry biomass and quality parameters, such as the digestibility (D-value) were collected simultaneously. The datasets were captured using a flying height of 50 m which provided a GSD of 0.7 cm for the photogrammetric imagery and 5 cm for the hyperspectral imagery. A rigorous photogrammetric workflow was carried out for all data sets aiming to determine the image exterior orientation parameters, camera interior orientation parameters, 3D point clouds and orthomosaics. The quantitative radiometric calibration included sensor corrections, atmospheric correction, and correction for the radiometric non-uniformities caused by illumination variations, BRDF correction and the absolute reflectance transformation. Random forest (RF) and multilinear regression (MLR) estimators were trained using spectral bands, vegetation indices and 3D features, extracted from the remote sensing datasets, and insitu reference measurements. From the FPI hyperspectral data, the 35 spectral bands and 11 spectral indices were used. The 3D features were extracted from the canopy height model (CHM) generated using RGB data. The most accurate results were obtained in the second measurement day (15th June) which was near to the optimal harvesting time and generally RF outperformed MLR slightly. When assessed with the leave-one-out-estimation, the best root mean squared error (RMSE%) were 8.9% for the dry biomass using 3D features. The best D-value estimation using RF algorithm (RMSE% = 0.87%) was obtained using spectral features. Using the estimators, we then calculated grass quality and quantity maps covering the entire test site to compare different techniques and to evaluate the variability in the field. The results showed that the low-cost drone remote sensing gave excellent precision both for biomass and quality parameter estimation if accurately calibrated, offering an excellent tool for efficient and accurate management of silage grass production.

The objective of the present study was to compare the synchronisation of the lying, feeding and standing behaviour of bulls housed in an uninsulated barn and in a pasture. Dairy (Ayrshire and Holstein-Friesian) and beef (Hereford) bulls were housed in June-July in either partly bedded pens in an uninsulated barn (PEN bulls; five groups of four or five bulls with 6.4 or 8.0 m2/bull) or in a pasture (PAS bulls; five groups of four or five bulls with 680, 1000 or 1250 m2/bull). The PEN bulls were fed grass silage ad libitum. For concentrate supplementation both the PEN and PAS bulls received rolled barley 4.4 kg dry matter/animal/day. The behaviour of the bulls was observed for two 24-h periods. The animals were 15–17 months old during the observations. Instantaneous sampling with a 6-min sampling interval (IS-6 min) was used originally for collecting the data. Lying, eating and standing (=all other activities performed in a standing posture) were recorded during the IS-6 observations. The Fleiss’ Kappa coefficient was used as an index of behavioural synchronisation. It was calculated for each of the groups of four or five animals across 24 (IS-60 min) and 12 (IS-120 min) time points within a 24-h day to increase the independency of the data points. The Fleiss’ kappa coefficient was calculated in two ways: combining all three behavioural classes of each observation day (KFO) as well as separately for lying (KFL), eating (KFE), and standing (KFS). The results were analysed using a selected multilevel linear mixed model. The IS-60 min (presented in the abstract) and IS-120 min results were essentially identical. PAS bulls displayed more synchronised behaviour than the PEN bulls (P

Correction FRONTIERS IN MICROBIOLOGY Volume: 12 Article Number: 693341 DOI: 10.3389/fmicb.2021.693341 Published: MAY 20 2021 The soil-borne plant pathogens cause serious yield losses and are difficult to control. In suppressive soils, disease incidence remains low regardless of the presence of the pathogen, the host plant, and favorable environmental conditions. The potential to improve natural soil disease suppressiveness through agricultural management practices would enable sustainable and resilient crop production systems. Our aim was to study the impact of autumn tillage methods and crop sequence on the soil carbon status, fungistasis and yield in boreal climate. The disease suppression was improved by the long-term reduced and no tillage management practices with and without crop rotation. Compared to the conventional plowing, the non-inversion tillage systems were shown to change the vertical distribution of soil carbon fractions and the amount of microbial biomass by concentrating them on the soil surface. Crop sequence and the choice of tillage method had a combined effect on soil organic carbon (SOC) sequestration. The improved general disease suppression had a positive correlation with the labile carbon status and microbial biomass. From the most common Fusarium species, the predominantly saprophytic F. avenaceum was more abundant under non-inversion practice, whereas the opposite was true for the pathogenic ones. Our findings furthermore demonstrated the correlation of the soil fungistasis laboratory assay results and the prevalence of the pathogenic test fungus Fusarium culmorum on the crop cereals in the field. Our results indicate that optimized management strategies have potential to improve microbial related soil fungistasis in boreal climate.

Fusarium head blight (FHB) and the mycotoxins produced by its causal agents in oats (Avena sativa L.) have become a growing problem in northern countries over the last decades. The development of resistant cultivars would offer a highly needed and economical solution to the problem. To tackle the high genotype×environment interaction of FHB, a combined analysis was carried out on eight greenhouse and 13 field experiments inoculated with DON-producing Fusarium species. Our data included 406 oat genotypes consisting of Nordic cultivars, breeding lines and potentially resistant gene bank accessions. High variation in the DON accumulation estimates in the material shows that the selection of genotypes with better resistance would be valuable. The greenhouse and field studies resulted in significantly different oat genotype susceptibility rankings for both DON and Fusarium infected kernels. The results obtained from the field experiments have more practical relevance for farmers and breeders for the identification of DON resistant cultivars than greenhouse screenings. Days to maturity and the plant height of the genotypes both significantly affected the Fusarium infections and DON in the field. The relationship between Fusarium infected kernels, DONand germination capacity provide an insight into the composition of genotypes with resistance. The core set of 30 oat genotypes, which were phenotyped in several experiments, provides valuable examples of both highly susceptible and moderately resistant oat genotypes.

In Finland, crop choices are limited, and cultivation is only possible in the regions where production risks and uncertainties are manageable. Climate change progresses rapidly at high latitudes and the thermal growing season is projected to become substantially longer in the future. This study aimed to monitor the regional shifts in major, secondary, minor and novel crops during 1996–2016 in Finland. We used long-term data from the Finnish Food Agency and evaluated changes in time to reach maturity of cultivars of model crops by using official variety trial data. Substantial changes were recorded in cultivation areas of crops, including expansion into new regions. Some of the traditional major crops such as oats (− 20%, i.e. − 75,700 ha from 1996 to 2016), barley (− 19%, − 105,700 ha) and potatoes (− 28%, − 4000 ha) have paved the way for emergent crops like faba beans (increase in area from 58 to 14,800 ha), peas (from 5700 to 13,400 ha), caraway (from 1900 to 18,400 ha) and spring oilseed rape (from 700 to 27,800 ha). Expansion per se was primarily enabled by climate warming, but success requires well-adapted cultivars, existing or emerging markets and industries or exports as well as motivating prices, policy support and valued ecosystem services.

The official variety trials at Rovaniemi, Finland (66.58°N, 26.01°E) in 1980–2017 show a substantial increase in dry matter yields (DMY) of timothy (Phleum pratense), meadow fescue (Festuca pratensis) and tall fescue (Festuca arundinacea), coinciding with a 156 °Cd increase in the average growing season Tsum and a 461 °Cd decrease in the average winter frost sum for the same period. The annual DMY of timothy was 3128, 4668, 8385 and 9352 kg ha-1 in the periods (P) 1980–1989 (P1), 1990–1999 (P2), 2000–2009 (P3), and 2010–2017 (P4). The first cut yielded 1792, 2166, 4008 and 4473, and the second cut 1337, 2503, 4378 and 4879 kg ha-1, respectively. Yields of meadow fescue followed a similar pattern. The first cut was about ten days and the second cut about one week earlier on P4 than on P1. Shorter snow cover period, milder winters, higher live ground cover of timothy in spring, and higher temperature sum during the growing season were most likely responsible for the yield increase. The results indicate a strong impact of climate change on DMY of perennial forage crops in the north.

In modern agriculture, the pesticides and the need to decrease their use is under discussion. Optimization methods and modelling tools are important research areas in this context. In this paper, data analysis, feature generation and selection in moving windows have been utilized for the evaluation of net blotch risk in barley. Two different datasets: The open data from the Finnish Meteorological Institute and the historical observation of the net blotch severity in different fields in Finland are combined with feature generation techniques. T-test is then applied to select the most statistically suitable features for prediction the net blotch risk from weather measurements. Analysis proceeds in moving data windows to indicate the most informative time period to predict the risk of net blotch during the growing season. Results show that the selection of the proper time instance and the length of data window may enhance strongly the potential performance of prediction methods for risk analysis on plant disease.

To govern better future landscape planning of high-latitude agricultural systems, it is necessary to understand fully the drivers that currently determine farmers’ land allocation to different crops. The aim of this study was to identify key farm and field characteristics that drive farmers’ land allocation, based on substantial datasets, and to benchmark findings for farmer perceptions with interviews. Focus was on characteristics that are easily monitored and facilitate development of a field optimization tool to support future implementation and monitoring processes for land use changes at farm and field scales. Drivers for land allocation to different crops varied between crop production and dairy farms, but less evidently between southern and northern regions. Crop choices differed among regions, being greater for southern than for northern farms, but also for farm types, dairy farms mainly cultivating on-farm feed. Some special crops, such as potato and reed canary grass, represented diversified land use in the northern region, but farmers tended to allocate such crops very strictly to certain types of field and did not diversify crop rotations. Interviewed farmers highlighted the complexity of land allocation and the interactive nature of the drivers. When comparing outcomes of the data analyses and farmers’ interviews, field size, distance from farm center and soil type were considered to be primary drivers for land allocation. Field shape, slope and land ownership, but only in the case of long-term contract periods, were hidden drivers, identified using statistical analyses, but were not specifically referred to by farmers. Proximity to waterways was the only field characteristic classified as unimportant. Farmers highlighted logistical advantages as an important driver for land allocation, which was confirmed by data analyses. A farmer’s justification process for land allocation is likely to be based on intergenerational transitive knowledge and concepts of operationally critical farm and field characteristics. Valuable, empirical information gained during this study needs to be coupled with the development of policy measures to develop effective future policy instruments that are not only practicable, but are easily implemented and cost-effective, while remaining coherent with other policies.

Climate change is projected to prolong Finland’s short growing season at both ends though warming autumns are not expected to benefit arable crops such as cereals, in contrast to warming springs. To test the veracity of this, ex-post and ex-ante approaches were applied to assess the past and future roles of autumns on cereal growth. Long-term multi-location data were used to assess the response of spring cereal cultivars on late harvests in the past. Future changes in temperature and precipitation, derived from the simulations performed with 28 global climate models under the RCP4.5 and RCP8.5 scenarios, compared with a baseline period, with mid-point year 1986, were averaged for three 30-year periods with mid-point years of 2025, 2055, and 2085. The phenological timing of growing seasons in a changing climate was simulated with the WOFOST. Warming autumns have insignificant potential for additional cereal yield gains. Even the latest maturing wheat cultivars would mature by the same time or earlier than currently when sown earlier. However, inter-annual variability in harvest times remains high, and hence many emerging risks may result from the elevated autumn precipitation in the future that will accompany delayed harvests. Means to benefit from warming autumns and mitigate their potential harmful impacts, like increasing nutrient leaching, erosion, and soil compaction, are needed. Post-harvest sowing of nutrient scavenging catch crops may provide the necessary soil cover, produce biomass, increase soil carbon, and protect soil from erosion and compaction. Hence, double cropping may be a viable alternative to safeguard sustainable high-latitude agriculture in a changing climate.

Climate change is triggering adaptation by people and wildlife. The speed and magnitude of these responses may disrupt ecological equilibria and potentially cause further biodiversity losses, but this has rarely been studied. Species inhabiting human-dominated landscapes may be particularly negatively affected by human adaptations to climate change. This could be, for example, the case of ground-nesting farmland birds, a group of highly vulnerable species that may be impacted by shifts in the timing of mechanical farming operations in response to climate change. Here we aim to explore whether trends in phenology of breeding ground-nesting birds differ from those of farming practices, and whether differences lead to the emergence of phenological mistiming with detrimental consequences to the birds. To achieve our objective, we tan linear mixed effects models using a 38 year dataset on onset of farming practices (i.e. sowing dates) and laying date of two endangered ground-nesting farmland birds (Northern lapwing and Eurasian curlew) in Finland. We found that timing of farming practices advanced slower than birds nesting phenology, with birds progressively starting nesting before fields are sown. These nests are at high risk of destruction from incoming sowing operations. The results highlight the importance of considering human adaptation responses, in addition to those of wildlife, for implementing species conservation in managed landscapes under climate change.

Land use change is a continuously on-going process that has many impacts on the environmental footprint of agriculture and especially on the biodiversity of agricultural landscapes. This study used field scale data from 1995 to 2011 (165,760 field parcels) on a study region that represents the prime crop production area of Finland, to assess how agricultural land use has changed since the launching of the EU Common Agricultural Policy. Six five-year crop rotation types were identified: cereal species monoculture, cereal monoculture, rotation with a break-crop, diverse crop rotation, perennial, non-permanent grassland rotation and environmental fallow rotation. Shifts in the frequencies of different crop rotation types and composition of their crop species were monitored. Furthermore, the contribution of different field characteristics, on a farmer's land allocation to different rotation types, was assessed. The ultimate goal was to understand whether land use changes, in general, have contributed to any increase in heterogeneity of landscapes and whether they have impacted diversity of crop rotation types. We found that different crop rotation types were applied on a farm, but that farmers have quite consistent drivers for land allocation to different rotation types; although, economic incentives influence the introduction, expansion and/or withdrawal of crops from rotations. The farmers' readiness to implement land use changes was dependent on farm size. There has been a shift towards lower shares of cereal species monocultures, grassland rotations and diverse crop rotations, while environmental fallow rotations have increased. According to the five-year rotation plans shared by 16 interviewed farmers, there was a noted desire for more diverse rotation types originating from adverse experiences with cereal monocultures and soil degradation; however, they were keen on reducing the number of environmental fallows and concentrating on food production. It is important to carry out follow-up studies to understand the impacts of the demonstrated and anticipated land use changes on biodiversity. Future policy development should benefit from a gained understanding of the drivers of farmers' decisions for facilitating unimpeded implementation.

Development time of immatures, fecundity, longevity and egg hatching of the predatory bug Macrolophus pygmaeus were studied over three years under artificial lighting in growth chambers to assess if light quality and photoperiod could explain its poor establishment in winter tomato crops at northern latitudes. In the first year, the effect of photoperiod was assessed using fluorescent lamps (FLs) at 16L:8D, 13L:11D, 11L:13D and 8L:16D. In the second year, the effect of light quality was assessed by producing the 16L:8D photoperiod with FLs, high pressure sodium lamps (HPSLs) and HPSLs complemented with red (R) light emitting diodes (LEDs) (11 + 5 h, HPSLs + R). In the third year, 16L:8D, 11L:13D, 8L:16D photoperiods were produced by HPSLs and 16L:8D with FLs. In all the treatments, females from the first, second and third generation oviposited, and their eggs hatched pointing that M. pygmaeus does not undergo reproductive diapause. In general, shorter photoperiods prolonged the development time of immatures regardless the light source, whereas fecundity increased with decreasing photoperiod irrespectively of light quality. In FLs, fecundity was significantly lower than in HPSLs alone or in HPSLs + R in one experiment, whereas light quality (FLs and HPSLs) did not affect fecundity in the other experiment. The results suggest that HPSLs as such do not adversely affect reproductive biology of the bug except for the pre-adult development time, and that part of the long photoperiod could be replaced with red LEDs without negatively affecting the bug’s biology. The 16-h light period produced by artificial lighting in year-round tomato production, however, is not optimal for the fecundity of M. pygmaeus, and fertility may also decrease. Further research is needed from greenhouse winter conditions where the bugs perceive and may respond also to the natural short photoperiod.

The addition of biochar to agricultural soils is recommended to improve soil functions and plant growth. However, due to high variability in the quality of biochar, its effects on soils and plants are likely to differ. We explored the impacts of pyrolysis temperature on the quality and usability of birch wood biochar as a soil amendment. The impact of three biochar types – pyrolysed at 300, 375 or 475 °C – on soil characteristics and the growth of lettuce ( Lactuca sativa), radish ( Raphanus sativus ), barley ( Hordeum vulgare ) and ryegrass ( Lolium perenne ) was investigated in a greenhouse experiment. In addition, the potential adverse effects of biochar on soil organisms (nematodes, earthworms, microbial biomass and activity) were studied. Biochar produced at the lowest temperature had initial transient negative effect on the germination and biomass of lettuce, while biochar produced at higher temperatures had no such effect. After five weeks, pyrolysis temperature had only a marginal influence on biochar-induced effects on soil pH, WHC, soil organisms and plant growth. Our results suggest that birch biochar, irrespective of pyrolysis temperature, has a parallel effect on plant biomass production and soil characteristics but the effect depends on plant type and biochar application rate.

Recent studies assessing agricultural policies, including the EU's Agri-Environment Scheme, have shown that these have been successful in attaining some environmental goals. In Finland, however, the economic situation of farms has dramatically fallen and hence, the actions do not result in social acceptability. Sustainable intensification is a means to combine the three dimensions of sustainability: environmental, economic and social. Here we introduce a novel land use optimization and planning tool for the sustainable intensification of high-latitude agricultural systems. The main rationale for the development of the tool was to achieve a systematic and comprehensive conception for land allocation across Finland, where field parcels vary substantially in their conditions. The developed tool has a three-step scoring system based on seven physical characteristics (parcel size, shape, slope, distance to the farm center and waterways, soil type and logistic advantages) and the productivity of field parcels. The productivity estimates are based on vegetation indices derived from optical satellite data. The tool allocates virtually all >1 million field parcels in Finland either to sustainable intensification, extensification or afforestation. The tool is dynamic in the sense that its boundary values for land allocation can be fixed according to changes in social targets and supporting policies. Additionally, it can be applied year after year by acknowledging new available data, e.g., on vegetation indices and field parcel rearrangements between farms. Furthermore, it can be applied to all farm types and across Finland. It is a tool for land use planning, implementation and monitoring, but its thorough implementation calls for further development of policy instruments, which are currently more supportive towards land sharing than land sparing activities.

SUMMARYThe current study aimed to evaluate breeding effect on nitrogen use efficiency (NUE), its components and some agronomic traits and disease resistance in barley by using extensive germplasm covering 72 landraces and 123 cultivars released since 1910. Trials were established in southern Finland with a modified strip-plot experimental design. Prior to sowing, blocks were placement fertilized with compound nitrogen : phosphorus : potassium (NPK) fertilizer (N-P-K: 20–3–8) at the rate of 35 and 70 kg N/ha and unfertilized plots were placed at the other end of the fertilization block. The germplasm collection was genotyped with 1536 single nucleotide polymorphism (SNP) markers and phenotyped during a 2-year field experiment in 2011/12. Independent of row type, a positive breeding effect was evident in NUE and for other plant N traits, except that grain N slightly decreased. Breeding has improved NUE by 0·08 kg/year (26% over the century). Nitrogen utilization and N uptake efficiencies were also improved by breeding as were straw length, lodging tolerance, grain yield and yield components, without any sign of levelling-off. Bred cultivars were more resistant to leaf-damaging diseases, especially to net blotch. The SNP data indicated no reduction in overall genetic diversity. However, genetic diversity differed along the barley chromosomes showing either reduced or increased diversity in certain regions when landraces were compared with modern varieties.

Generalist herbivores can face many challenges when choosing their host plant. This can be particularly difficult if their choice and performance are affected by host experience. Greenhouse whitefly, Trialeurodes vaporariorum Westwood (Hemiptera: Aleyrodidae), is an invasive generalist herbivore, which has established in year-round greenhouses at northern latitudes where it cannot overwinter outdoors. It mainly uses crops such as cucumber (Cucumis sativus L.), tomato (Solanum lycopersicum L.), and ornamentals as host plants. However, every summer the insect escapes greenhouses and is exposed to natural vegetation. We evaluated the performance of T. vaporariorum on diverse vegetation outside greenhouses after prolonged experience of greenhouse crops. First, we surveyed the vegetation near infested greenhouses. Development success of the insect differed among wild hosts. We identified five new hosts among 12 plant species that bore pupae and were thus considered suitable as the insect's host plants. Members of the Urticaceae and Onagraceae were the most preferred and frequently inhabited by all insect life stages. The highest abundance of insects occurred in plots with low plant species richness, independent of plant family in these habitats. We then studied experimentally the impact of 1 year of preconditioning to one of three common greenhouse crops, cucumber, tomato, or poinsettia (Euphorbia pulcherrima Willd. ex Klotzsch), on the performance of the preconditioned adults and their progeny on four wild plants. Adults from tomato and poinsettia preferred the novel host species over the species to which they were preconditioned. The whitefly population preconditioned to cucumber was the most fecund on all offered hosts. We conclude that generalist herbivores can have large variation in performance, despite polyphagy, on novel hosts as shown by the variable abundance of T. vaporariorum pupae among outdoor hosts. Furthermore, performance of whiteflies on natural vegetation was affected by experience on greenhouse crops. Based on our observations, we provide insights and recommendations for pest management.

Glyphosate (N-(phosphonomethyl) glycine), a commonly used herbicide in agriculture can leach to deeper soil layers and settle in surface- and ground waters. To mitigate the leaching of pesticides and nutrients, biochar has been suggested as a potential soil amendment due to its ability to sorb both organic and inorganic substances. However, the efficiency of biochar in retaining agro-chemicals in the soil is likely to vary with feedstock material and pyrolysis conditions. A greenhouse pot experiment, mimicking a crop rotation cycle of three plant genera, was established to study the effects of pyrolysis temperature on the ability of birch (Betula sp.) wood originated biochar to reduce the leaching of (i) glyphosate, (ii) its primary degradation product AMPA and (iii) phosphorus from the soil. The biochar types used were produced at three different temperatures: 300 °C (BC300), 375 °C (BC375) and 475 °C (BC475). Compared to the control treatment without biochar, the leaching of glyphosate was reduced by 81%, 74% and 58% in BC300, BC375 and BC475 treated soils, respectively. The respective values for AMPA were 46%, 39% and 23%. Biochar had no significant effect on the retention of water-soluble phosphorus in the soil. Our results corroborate earlier findings on pesticides, suggesting that biochar amendment to the soil is a promising way to reduce also the leaching of glyphosate. Importantly, the ability of biochar to adsorb agro-chemicals depends on the temperature at which feedstock is pyrolysed.

Global warming is likely to prolong the growing season at high latitudes where the brevity of the growing season currently limits crop growth and yields. A longer growing season, elevated atmospheric carbon dioxide (CO2), dedicated plant breeding, and adjusted agronomic practices could open new avenues to increase yield potential while reducing risks of various abiotic stresses such as inadequate precipitation. We used statistical and simulation modelling approaches to determine whether future estimates for elevated yield potentials match projected changes (according to the high-emission SRES A2 scenario) in precipitation patterns and volumes, essential prerequisites for high-latitude rainfed agroecosystems. The statistical approach used long-term data sets to estimate future changes in yield potentials resulting from extension of the growing season and new, better adapted cultivars through plant breeding. Estimated increases in potential yield and biomass were 20–54 kg ha−1 per annum for grain or seed yield, depending on crop, excluding the effects of elevated (CO2). We also applied a crop simulation approach, using the WOFOST model, with barley (Hordeum vulgare L.) as a test crop, to simulate the impact of changing mean climate and the CO2 fertilisation on biomass development and yield formation under optimal nutrient supply, favourable soils, and complete plant protection. Elevated CO2 alleviated the drought effects. However, drought risk increased as the projected increases in precipitation seemed insufficient to meet the increasing needs of the potentially higher crop biomasses. Therefore, irrigation systems need to be developed to realise the future higher yield potentials in northern European agricultural systems.

Drones offer entirely new prospects for precision agriculture. This study investigates the utilisation of drone remote sensing for managing and monitoring silage grass swards. In northern countries, grass swards are fertilised and harvested three times per season when aiming to maximise the yield. Information about the grass quantity and quality is necessary to optimise these operations. Our objectives were to investigate and develop machine-learning techniques for estimating these parameters using drone photogrammetry and spectral imaging. Trial sites were established in southern Finland for the primary growth and regrowth of grass in the summer of 2017. Remote-sensing datasets were captured four times during the primary growth season and three times during the regrowth period. Reference measurements included fresh and dry biomass and several quality parameters, such as the digestibility of organic matter in dry matter (the D-value), neutral detergent fibre (NDF), indigestible neutral detergent fibre (iNDF), water-soluble carbohydrates (WSC), the nitrogen concentration (Ncont) in dry matter (DM) and nitrogen uptake (NU). Machine-learning estimators based on random forest (RF) and multiple linear regression (MLR) methods were trained using the reference measurements and tested using independent test datasets. The best results for the biomass estimation, nitrogen amount and digestibility were obtained when using hyperspectral and 3D data, followed by the combination of multispectral and 3D data. During the training process, the best normalised root-mean-square errors (RMSE%) were 14.66% for the dry biomass and 12% for fresh biomass; the best RMSE% values for NU, the D-value and NDF were 13.6%, 1.98% and 3% respectively. For the primary growth, the accuracies of all quality parameters were better than 20% with the independent test datasets; for the regrowth, the estimation accuracies of the D-value, iNDF, NDF, Ncont and NU were better than 20%. The results showed that drone remote sensing was an excellent tool for the efficient and accurate management of silage production.

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